Proposed Extension to Darwin Core for People and their Roles in the Curation of Physical and Digital Objects
Bibliographic record
Abstract
The Global Biodiversity Information Facility's 2017-2021 implementation plan includes an item with a scheduled start in 2017 to develop mechanisms to support and reflect the skills, expertise and experience of individual and organizational contributions to their network. This includes revision of their identity management system and integration of Open Researcher and Contributor IDs (ORCID). Likewise, the Joint TDWG/Research Data Alliance Interest Group on metadata standards for attribution of physical and digital collection stewardship seeks to develop metadata standards for attributing curatorial actions. Here, I propose a lightweight extension to Darwin Core to accommodate new terms for agent identifiers and their roles in the curation of physical and digital objects. In parallel, I propose shared mechanisms and codebases to help parse and disambiguate agent names in the existing Darwin Core terms: recordedBy, identifiedBy, and scientificNameAuthorship. The solutions to deal with legacy data must fit within the Biodiversity Data Quality Interest Group's recently proposed conceptual framework and be made available to individual museums, and national and international aggregators of biodiversity data. A case study using occurrence data from the Canadensys network will reveal the challenges in reconciling people names and will uncover exciting opportunities for engagement when people are shown the impact they have on their research and collections communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".